Classification of active sonar echoes using a one-class classification technique
dc.contributor.author | Nguyen, Binh | |
dc.contributor.author | Kouzoubov, Alexei | |
dc.contributor.author | Wood, Shane | |
dc.date.accessioned | 2018-02-15T22:53:38Z | |
dc.date.available | 2018-02-15T22:53:38Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Acoustics 2017 | en_US |
dc.identifier.uri | https://dspace.nal.gov.au/xmlui/handle/123456789/799 | |
dc.description.abstract | A typical approach to data classification based on machine learning algorithms is binary classification. This in-volves the classifier to be trained using representative data sets provided from two object classes. In reality, da-ta from one of the classes may be not well-defined or readily available and so the one-class classification tech-nique is gaining popularity. In this research we apply this method to the problem of classification using active sonar echoes from different classes of objects. A one-class classification research tool was developed in Matlab® to implement several one-class classification techniques found in literature. The tool was applied to three sets of data: simulated, laboratory and at-sea. The performance of the selected classifiers on different da-ta sets will be discussed in this paper. | en_US |
dc.language.iso | en | en_US |
dc.title | Classification of active sonar echoes using a one-class classification technique | en_US |
dc.type | Working Paper | en_US |
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